Why Promising Compounds Still Fail: Drug Discovery Shifts Toward Earlier Translatability Testing

Compounds that show strong activity against a target in an initial assay frequently become the focus of an optimization program, yet that early promise often fails to hold as testing advances. A molecule performing well in a biochemical or cellular assay may behave very differently once exposure, cell permeability, intracellular localization, target abundance, protein turnover or toxicity enter the picture. This gap between early activity and performance in more realistic biological systems remains a persistent challenge in drug discovery, according to an industry analysis released in September 2026.

Potency Is Only Part of the Pharmacological Picture

A molecule can be highly active against its target and still carry poor solubility, weak pharmacokinetics, metabolic instability, inadequate selectivity or other liabilities. When potency becomes the dominant measure of progress, those issues can receive attention too late.

Discovery teams are therefore examining a wider set of pharmacological characteristics earlier in the process. Binding kinetics and the duration of target engagement, for example, can provide information that a potency value alone cannot. A highly potent compound may be less attractive if its interaction with the target is too transient or if engagement is not sustained in a relevant biological context.

Biomarker planning offers another perspective. Thinking about biomarkers during discovery can help teams consider how they may determine whether the intended mechanism is operating in vivo and, later, in patients.

The result is a different approach to optimization in which potency is considered alongside selectivity, drug metabolism and pharmacokinetics (DMPK), absorption, distribution, metabolism and excretion (ADME) properties, exposure, target engagement and safety signals. Trade-offs are unavoidable: a compound with somewhat lower potency may still be the better choice if it provides stronger exposure, more durable target engagement or a cleaner overall profile. Additional gains in potency may have limited value if important liabilities remain. Multiparameter optimization gives medicinal chemists a way to evaluate those properties together and provides a more realistic basis for deciding which compounds deserve further work.

Better Models Reveal Problems That Simple Assays Miss

A biochemical assay can establish whether a molecule interacts with its intended target, but it cannot reproduce everything that influences pharmacology inside a cell, tissue or organism. These differences matter as testing moves into more physiologically relevant systems. Cell permeability may determine whether enough compound reaches an intracellular target. Target abundance and protein turnover can influence the duration of a response. Exposure, tissue environment and toxicity can alter behavior that looked straightforward in an earlier assay.

The challenge can be greater with newer modalities. Targeted protein degraders (搜索)搜索该药品 depend on more than target binding, and antibody-drug conjugate查看药品详情 (ADC) payloads bring additional considerations related to delivery, intracellular behavior and biological context. As discovery chemistry expands beyond conventional small molecules, teams need evidence that a mechanism continues to work as the biological model becomes more realistic.

More sophisticated models provide that evidence, but they also produce more complicated data sets. A modern discovery program may need to consider biological activity, selectivity, target engagement, ADME-related properties and other measurements simultaneously. Generating the data is only part of the job; teams must also decide which findings are important enough to change the next experiment. That places greater value on how quickly information moves between chemistry and biology.

Shortening the Distance Between an Experiment and the Next Decision

Drug discovery is often described as a design-make-test cycle, but in practice design, synthesis and testing can still occur in relatively separate workflows. Chemists make compounds, biology teams test them, and results return to chemistry for the next round of design. When that feedback takes too long, the next chemistry cycle may already be underway, and an important result can arrive after the team has made another set of design decisions.

Closer integration makes the data more useful while there is still an opportunity to act. If testing reveals poor permeability, unexpected biological behavior, weak target engagement or an ADME liability, chemists can investigate the finding in the next design cycle rather than several cycles later.

Direct-to-Biology workflows are one way to tighten this connection by linking compound design, synthesis and biological testing more closely. Faster testing is useful, although speed alone is not the point; the larger benefit comes from allowing new chemical and biological information to influence what scientists make next.

Tao Guo, Ph.D., senior vice president, Research Chemistry Services, Integrated Program Management, at WuXi AppTec查看公司主页, has described medicinal chemistry as becoming more involved from the beginning of discovery programs. Chemists and biologists collaborate on target hypotheses, validation and experimental design, allowing chemical feasibility and biological understanding to develop together and making it easier to recognize when the evidence for a chemical series is weakening.

Know When a Chemical Series Is Telling You Something

Deciding what to stop can be as important as deciding what to advance, and the quality of the chemical matter entering a program has a direct effect on those decisions. A larger screening library does not necessarily provide better starting points; diversity, relevance and compound quality help determine what chemists have available once hits move into optimization.

Several chemical series may initially appear promising, and potency can make one stand out early. As ADME, exposure, selectivity and other information accumulate, rankings can change. Persistent liabilities deserve attention: repeated problems with solubility, metabolism, exposure or selectivity may indicate that a series has limitations another round of chemistry is unlikely to resolve. Rapid triage can help teams identify that pattern earlier, while higher-quality library design, multiparameter optimization and earlier testing provide a broader evidence base for deciding whether continued exploration is justified.

Better Translatability Starts Before Development

No assay or technology can predict with certainty whether an early discovery compound will become a successful drug. Discovery teams can, however, learn more about the risks before large amounts of time and resources are committed. A strong potency result needs to be considered in the context of the molecule's wider pharmacological behavior, mechanisms need to hold up in increasingly relevant biological systems, and new findings need to reach the scientists making the next design decision while those findings can still influence the program.

There is also a practical side to translatability. Process feasibility and scalability have traditionally received more attention after candidate selection, and earlier consideration of these factors can reveal compounds that may be difficult to synthesize, purify or manufacture reproducibly. A molecule can therefore look promising by the standards of an early experiment and still be poorly suited for what comes next.

Improving translatability depends on recognizing those gaps sooner. Better information, interpreted at the right point in the discovery process, gives teams more opportunity to improve a compound, change direction or move resources to a stronger chemical series. The question is ultimately broader than whether a compound performs well in the experiment in front of us; what matters is whether the evidence continues to support its path toward a viable medicine.